One Month Hands-on Training program on Python: From Basic to Advanced
| Schedule of Workshop | Topic |
|---|---|
| 2024-10-04 | *Installation of R and R Studio *Analysis of variances for RBD design & alpha RBD design *Pooled ANOVA analysis *AUGMENTED DESIGNS ANALYSIS *SPLIT PLOT DESIGN ANALYSIS *ALPHA LATTICE DESIGN ANALYSIS *VARIABILITY PARAMETERS ANALYSIS *CORRELATION PLOT ANALYSIS *Principal component plot analysis (PCA) *Cluster plot analysis *BOX PLOTS *BOX PLOTS WITH SIGNIFICANCE LETTERS *Violin plot analysis *Heat map analysis *AMMI and GGE biplot analysis |
| 2024-11-23 | Installation of R and RStudio, ANOVA Analysis: RBD Design, Pooled ANOVA, Augmented Design Analysis, Split Plot Design Analysis, LSD Test, Duncan Test, Tukey’s Test, GCV, PCV, h2, GA, Correlation Plot Analysis, Principal Component Plot Analysis, Box Plot Analysis, Box Plots with Significance Letters ( Using Tukey's test), Violin Plot Analysis, Heat Map Analysis. |
| 2025-01-25 | Day 1: Basics of Machine Learning: Topics:- 1. What is ML? Importance and applications in various fields. 2. Types of ML: Supervised, Unsupervised, and Reinforcement Learning. 3. ML workflow: Problem definition, data collection, preprocessing, training, and evaluation. 4. Tools and platforms: Overview of Python, Jupyter Notebook, and essential ML libraries (Scikit-learn, Pandas). Activity:- 1. Discuss examples of ML systems students interact with daily (Facebook, Google Search, YouTube recommendations). Hands-On:- 1. Set up Jupyter Notebook. 2. Load and visualize a dataset (Real world dataset) using Pandas and Matplotlib. Day 2: Data Handling and Preprocessing: Topics:- 1. Data cleaning: - Handling missing values, outliers, and categorical data. 2. Feature scaling: - Normalization and standardization. 3. Data splitting: - Train-test split and cross-validation. Hands-On:- 1. Preprocess a dataset (Real world dataset) for ML. 2. Perform exploratory data analysis (EDA) to identify trends and anomalies. Day 3: Supervised Learning Fundamentals: Topics:- 1. Linear Regression, Logistic Regression, and Decision Trees. 2. Metrics for model evaluation (accuracy, RMSE, confusion matrix). Hands-On:- 1. Real-time data prediction model deployment using Linear Regression. 2. Apply Logistic Regression for binary classification (Real world dataset classification deployment model). Day 4: Unsupervised Learning: Topics:- 1. Clustering: K-Means and Hierarchical Clustering. 2. Dimensionality reduction: PCA (Principal Component Analysis). Hands-On:- 1. Perform customer segmentation using K-Means clustering. 2. Visualize high-dimensional data using PCA. Day 5: Deep Learning Basics: Topics:- 1. Introduction to Neural Networks (NNs). 2. Layers, activation functions, and optimizers.Frameworks: 3. Working Libraries: - TensorFlow, Keras, and PyTorch overview. Hands-On:- 1. Create a CNN to classify images from the Real-Time Image dataset. Day 6: Big Data Tools & Cloud Platforms: Topics:- 1. Introduction to Big Data tools and ecosystems. 2. Overview of cloud platforms (AWS). 3. Setting up and working with AWS EC2 Instances. Hands-On:- 1. Launch an EC2 instance on AWS and configure Jupyter Notebook. 2. Analyze large datasets using Pandas and Spark. Day 7: MS Excel Data Management: Topics:- 1. Sorting and Filtering: Organizing data efficiently. 2. Conditional Formatting: Highlighting data based on rules. 3. Data Validation: Create dropdown lists and restrict inputs. Hands-On:- 1. Apply conditional formatting to highlight different-different analysis on real-time data analysis. Day 8: MS Excel Essential Formulas and Functions: Topics:- 1. Basic formulas: SUM, AVERAGE, COUNT, MIN, MAX. 2. Logical functions: IF, AND, OR. 3. Text functions: CONCATENATE, LEFT, RIGHT, MID, LEN. 4. Lookup functions: XLOOKUP, VLOOKUP and HLOOKUP. Hands-On:- 1. Real time Data project using conditional (IF, ELSE, IFS, etc) functions. 2. Use of XLOOKUP, VLOOKUP in real-time data analysis. Day 9: Designing Professional Slides in MS-PowerPoint: Topics:- 1. Using design themes and templates effectively. 2. Slide master: Customizing global styles for a consistent look. 3. Working with colors, fonts, and alignment for aesthetics. 4. Adding and customizing SmartArt for visual representation of information. Hands-On:- 1. Design a slide with a custom theme using Slide Master. 2. Use SmartArt to create an organizational chart or process flow. Day 10: Career Guidance, Q&A, and Future Opportunities: Career Paths: - Data Analyst vs. Data Scientist vs. Machine Learning Engineer. Technical Skills: - Python, SQL, MS-Excel, Tableau, Power BI, Statistics, etc. Resume Building: - ATS-friendly resume, Summary, Skills, Experience, Projects and Education. Doubt Resolution and Pathway Planning.
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| 2025-03-16 | Installation of R and RStudio Analysis of variances for RBD design & alpha RBD design Pooled ANOVA analysis LSD, Duncan, Tukey's test Augmented Design Analysis Split Plot Design Analysis Alpha Lattice Design Analysis Variability Parameters Analysis Correlation Plot Analysis Principal Component Analysis Box Plot Analysis Box plot With Significance Letter Violin Plot Analysis Heat Map Analysis AMMI and GGE biplot analysis |
| 2025-04-26 | *Installation of R and R Studio *Analysis of variances for RBD design & alpha RBD design *Pooled ANOVA analysis *AUGMENTED DESIGNS ANALYSIS *SPLIT PLOT DESIGN ANALYSIS *ALPHA LATTICE DESIGN ANALYSIS *VARIABILITY PARAMETERS ANALYSIS *CORRELATION PLOT ANALYSIS *Principal component plot analysis (PCA) *Cluster plot analysis *BOX PLOTS *BOX PLOTS WITH SIGNIFICANCE LETTERS *Violin plot analysis *Heat map analysis *AMMI and GGE biplot analysis |
| 2025-05-25 | One Map Analysis |
| 2025-06-25 | Installation of R and RStudio Analysis of variances for RBD design & alpha RBD design Pooled ANOVA analysis LSD, Duncan, Tukey's test Augmented Design Analysis Violin Plot Analysis Heat Map Analysis Split Plot Design Analysis Alpha Lattice Design Analysis Variability Parameters Analysis Correlation Plot Analysis Principal Component Analysis Box Plot Analysis AMMI and GGE biplot analysis Linkage map construction QTL mapping GWAS analysis |
| 2025-09-05 | Installation of R and RStudio Analysis of variances for RBD and CRD designs Two Factor RBD & CRD ANOVA and LSD Test Three Factor RBD & CRD ANOVA and LSD Test Augmented Design Analysis Split Plot Design Analysis (2/3 Factors) Split Plot Design Analysis (4 Factors) Alpha Lattice Design Analysis Variability Parameters Analysis Correlation Plot Analysis Principal Component Analysis Box Plot Analysis Box plot With Significance Letter Violin Plot Analysis Heat Map Analysis AMMI and GGE biplot analysis
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| 2025-10-16 | ✔ Linkage Mapping ✔ QTL Mapping ✔ GWAS Analysis ✔ Genomic Region Annotation |
| 2025-11-14 | *Installation of R and R Studio *Analysis of variances for RBD design & alpha RBD design *Pooled ANOVA analysis *AUGMENTED DESIGNS ANALYSIS *SPLIT PLOT DESIGN ANALYSIS *ALPHA LATTICE DESIGN ANALYSIS *VARIABILITY PARAMETERS ANALYSIS *CORRELATION PLOT ANALYSIS *Principal component plot analysis (PCA) *Cluster plot analysis *BOX PLOTS *BOX PLOTS WITH SIGNIFICANCE LETTERS *Violin plot analysis *Heat map analysis *AMMI and GGE biplot analysis |
| 2025-12-19 | 1. Installation of R and RStudio 2. Analysis of variances for RBD design 3. Analysis of variances for CRD design 4. Factorial RBD design ANOVA 5. Factorial CRD design ANOVA 6. LSD and DUNCAN Test 7. Variability Parameters Analysis 8. Correlation Plot Analysis 9. Box Plot Analysis |
| 2026-01-21 |
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| 2026-02-20 | Correlation Plot Analysis Principal Component Analysis Box Plot Analysis Box Plot + Tukey HSD Violin Plot Analysis Cluster Plot Bar-Plot HeatMap Plot Analysis AMMI Biplot Analysis GGE Biplot Analysis R packages: corrplot, ggcorrplot, PerformanceAnalytics, GGally, FactoMineR, factoextra, ggplot2, Base R, agricolae, dplyr, pheatmap, tidyverse, metan, devtools, kableExtra, cowplot, magrittr etc. |
| 2026-06-01 | Installation of R Software’s and Introductory R Analysis of variances for RBD design Analysis of variances for CRD design Tukey's HSD and LSD Test Shapiro–Wilk test Pooled Analysis of variances (ANOVA) Two Factor RBD & CRD ANOVA Three Factor RBD & CRD ANOVA Augmented Design Analysis Split Plot Design Analysis (2/3 Factors) Split Plot Design Analysis (4 Factors) Alpha Lattice Design Analysis Variability Parameters Analysis Correlation Plot Analysis Principal Component Analysis Box Plot Analysis Box Plot + Tukey’s HSD Violin Plot Analysis HeatMap Plot Analysis Cluster Plot Bar-Plot
R packages: agricolae, augmentedRCBD, DioBioresearch, Variability, corrplot, ggcorrplot, PerformanceAnalytics, GGally, FactoMineR, factoextra, ggplot2, Base R, dplyr, pheatmap, tidyverse, metan, devtools, kableExtra, cowplot, magrittr etc. |
| 2026-07-06 | 1. Installation of R Software’s and Introductory R 2. Normality (Shapiro–Wilk) Test 3. Linkage Map Construction using R 4. QTL Mapping 5. Data handling with TASSEL 6. Genome wide association analysis (MLM, GLM, FramCPU, Blink etc. Models) 7. Linkage Disequilibrium (LD) decay plot Construction 8. Phylogenetic Tree construction 9. Kruskal-Wallis test for identification of significant alleles 10. Identification of Candidate Genes
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| 2026-09-13 | 1. Installation of R Software’s and Introductory R 2. Analysis of variances for RBD design 3. Analysis of variances for CRD design 4. Tukey's HSD , Duncan and LSD Test 5. Shapiro–Wilk test 6. Pooled Analysis of variances (ANOVA) 7. Two Factor RBD & CRD ANOVA 8. Three Factor RBD & CRD ANOVA 9. Augmented Design Analysis 10. Split Plot Design Analysis (2/3 Factors) 11. Alpha Lattice Design Analysis 12. Variability Parameters Analysis 13. Correlation Plot Analysis 14. Principal Component Analysis 15. Box Plot Analysis 16. Box Plot + Tukey’s HSD 17. Violin Plot Analysis 18. HeatMap Plot Analysis 19. K-mean Cluster Plot 20. Bar-Plot 21. Line x Tester Analysis 22. AMMI and GGE Analysis etc. R packages: agricolae, augmentedRCBD, DioBioresearch, Variability, corrplot, ggcorrplot, PerformanceAnalytics, GGally, FactoMineR, factoextra, ggplot2, Base R, dplyr, pheatmap, tidyverse, metan, devtools, kableExtra, cowplot, magrittr etc.
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| 2026-09-27 | DAY 01 — AI & MACHINE LEARNING FUNDAMENTALS Understanding AI & Machine Learning Build Your First ML Prediction Model DAY 02 — PYTHON FOR AI & MACHINE LEARNING Python Fundamentals for ML Build a Student Marks Analyzer DAY 03 — DATA HANDLING WITH NUMPY & PANDAS Working with Real-World Datasets Data Cleaning & Analysis DAY 04 — DATA VISUALIZATION & INSIGHTS Create Powerful Data Visualizations Discover Patterns from Data DAY 05 — YOUR FIRST MACHINE LEARNING MODEL Linear Regression Predict Real-World Outcomes DAY 06 — MACHINE LEARNING CLASSIFICATION Classification with ML Build a Student Pass/Fail Predictor DAY 07 — AI FOR TEXT & SPAM DETECTION Text Classification with Machine Learning Build Your Own Spam Detector DAY 08 — HOW ACCURATE IS YOUR AI? Model Evaluation Accuracy, Precision, Recall & Confusion Matrix DAY 09 — FIND THE MOST IMPORTANT FEATURES Feature Selection Introduction to PCA & Dimensionality Reduction DAY 10 — BUILD YOUR OWN AI/ML PROJECT |
| 2026-10-25 | 1. The Science Behind Mushrooms 2. Mushroom Cultivation: Types, Techniques & Technologies 3. Mushroom Disease & Contamination Management 4. Post-Harvest Technology & Shelf-Life Management 5. Mushroom Processing & Value Addition: From Mushroom to Bio-products 6. From Lab to Farmer: Translating Mushroom Research into Practice 7. From Farmer to Market: Marketing & Market Linkages 8. Economics of Mushroom Farming: Cost, Returns & Benefit–Cost Ratio 9. Mushroom Entrepreneurship: From Farm to A Successful Business |